description:

FM radio station locations. More information can be found at http://www.fcc.gov/encyclopedia/broadcast-radio-links

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Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government or the Federal Communications Commission (FCC). No warranty expressed or implied is made by the FCC as to the accuracy of the data and related materials. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the FCC in the use of this data, or related materials. Not to be used for navigation, for informational purposes only. See full disclaimer for more information.

FM radio station locations. More information can be found at http://www.fcc.gov/encyclopedia/broadcast-radio-links

Constraints:
Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government or the Federal Communications Commission (FCC). No warranty expressed or implied is made by the FCC as to the accuracy of the data and related materials. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the FCC in the use of this data, or related materials. Not to be used for navigation, for informational purposes only. See full disclaimer for more information.

Radio Frequency Measurements for Selected Manufacturing and Industrial Environments using a PN Code Sounding methodology. The resulting measurements include complex impulse responses and spectrum analysis traces. Complex impulse responses were validated using both ray tracing and an outdoor two-ray reference facility. This data is meant to serve as a reference of how radio waves at 2.4 GHz and 5 GHz propagate in industrial environments.

mn367/radio-dataset-test dataset hosted on Hugging Face and contributed by the HF Datasets community

Modern, industrial use cases for wireless communications are related to mobile applications such as moving robotics in industrial environments. For the design of communication systems, the behavior of the radio channel, especially over time, is of great importance. Most of the existing data sets for industrial radio channels originate from static measurement procedures, containing an arbitrary subset of the environment. Our data set for industrial radio channels originates from a measurement campaign that equipped a moving measurement setup to continuously measure the radio channel over time with a very high measurement frequency of 1kHz. By measuring channel frequency responses with this temporal resolution, the correlations in time and frequency of the radio channel are represented in the data set. With the help of a constant vehicle trajectory and a time-varying environment due to added obstacles between measurement repetitions, the impact of a time-varying environment (10 measurement scenarios in total) is included in the data set as well.

"Country music" and "Rock / alternative / indie music" are the top two answers among U.S. consumers in our survey on the subject of "Preferred radio content by genre".Find this and more survey data on preferred radio content by genre in our Consumer Insights tool. Filter by countless demographics, drill down to your own, hand-tailored target audience, and compare results across countries worldwide.

The reach of online radio in the United States has increased sharply in the last decade, with 75 percent of the U.S. population having listened to the online radio in the past month in 2023, compared to 45 percent ten years previously. Weekly online radio consumption among U.S. listeners has also increased, growing from 12 percent in 2007 to 70 percent in 2023.

This is a data set for Radio Frequency fingerprinting, which is a kind of identification of wireless devices based on their intrinsic physical features. The data set is composed by GSM bursts collected from 12 GSM mobile phones while transmitting. The samples have been collected using a Software Defined Radio with a sample rate at 20 MS/s. The content information has been removed from the bursts to remove the risk of bias due to content. The data set is in MATLAB format.

This dataset is provided as supplementary material of the work entitled "Gated Recurrent Unit Neural Networks for Automatic Modulation Classification with Resource-Constrained End-Devices", published in IEEE Access Journal.

This dataset includes over-the-air measurements of real radio signals modulated with 11 different modulations. These signals were generated by a transmitter formed by a USRP B210 connected to a computer with GNU Radio. In order to implement the different transmitters, we used the source code and same data sources with which RadioML2016.10a was generated. It should be noted that an error with AM modulations, solved in later versions of the RadioML dataset, was also corrected for our dataset generation.

On the receiver side, we used the MIGOU platform to record the signals. This is a low-power experimental platform with Software-Defined Radio (SDR) capabilities that has been specifically designed to address the hardware architectural constraints that limit Cognitive Radio (CR) research and experimentation with low-power end-devices. This platform was configured to sense a communication channel and send the raw I/Q samples to a computer that properly stores them.

All measurements were carried out indoors, in an office environment. Specifically, measurements were taken at two distances from the transmitter, 1 and 6 meters, which corresponds to average Signal-to-Noise Ratios (SNRs) of 37 dB and 22 dB respectively. All these recorded I/Q signals were divided and formatted into 128×2 vectors, which were individually normalized. Finally, 400,000 normalized vectors were included for each modulation-SNR (MOD-SNR) pair in the dataset, resulting in a total of 8.8 million vectors.

For more details of the configuration parameters, the normalization method and the materials used, please refer to the work mentioned in the first paragraph.

According to the source, there were 15,377 commercial radio stations in the United States in 2022, 12 down from the previous year but still more than double the amount of stations in 1970.

The DroneDetect dataset consists of 7 different models of popular Unmanned Aerial Systems (UAS) including the new DJI Mavic 2 Air S, DJI Mavic Pro, DJI Mavic Pro 2, DJI Inspire 2, DJI Mavic Mini, DJI Phantom 4 and the Parrot Disco. Recordings were collected using a Nuand BladeRF SDR and using open source software GNURadio. There are 4 subsets of data included in this dataset, the UAS signals in the presence of Bluetooth interference, in the presence of Wi-Fi signals, in the presence of both and with no interference. 3 flight modes are captured - switched on, hovering and flying.

Country music stations were most frequent radio formats in 2022, with 2,178 unique stations in the United States that year. Also popular were news and talk shows, with over 2,000 stations falling in this category.

This is signal energy data for the radio frequency of 98.3 MHz collected in India from USRP device. The sampling rate is 5M. The data was collected for 10 seconds.

This data set is the capture of the Radio Frequency emissions from 9 IoT devices using an USRP Software Defined Radio. The data set is in MATLAB format and it stores the IQ samples of the signals in space. The data set can be used for experimental and analysis on Radio Frequency identification and authentication.

This collection is about the social history of radio. The research was carried out in collaboration with the Radio Museum of Lafkos Pelion, contains 15 individual interviews and a group interview with radio professionals (from ERA Volos, the Municipal Radio of Volos, the private radio and pirate stations) and interviews with listeners from Greece and Eastern Europe. The collection also contains multimedia presentations of each interview in powerpoint format. Non-probability: Availability Face-to-face interview

This collection is about the social history of the radio. Although it has been pushed aside by television, it has played a vital and unique role in the entertainment, information and communication of everyday people. This research was conducted in collaboration with the Radio Museum at Lafkos, Pelion and includes 16 individual interviews of listeners, as well as professionals in the radio industry. Non-probability: Availability

In 2023, radio advertising revenue in the United States and Canada will amount to an estimated 16.2 billion U.S. dollars. The source projected that the value will grow by around 3.7 percent in four years, reaching 16.8 billion dollars by 2027.

U.S. radio stations' ad revenues According to another source, U.S. over-the-air and online radio stations' ad revenues will amount to an estimated 13.2 billion dollars in 2023, down from 14 billion dollars in the previous year – boosted by political ads. The segment's results remained below pre-pandemic heights. Meanwhile, U.S. local radio's digital ad revenues were set to reach an all-time high in 2023: 2.1 billion dollars. Half a decade earlier, in 2018, the value amounted to less than 40 percent of that.

U.S. top radio ad categories Three industries dominated the investments in radio spots in the U.S. in 2022. Retail's ad expenditure on the medium stood just below 400 million dollars. The miscellaneous services and amusements and the government, politics, and organizations segments spent about 365 million and 339 million dollars on radio ads, respectively. The profile of the audiences those brands pursue is also well-known. As of mid-2023, most radio listeners in the U.S. were aged between 35 and 64 years.

In the first quarter of 2020, the average U.S. consumer spent 99 minutes daily with radio content, or one hour and 39 minutes per day, down three minutes from the corresponding quarter of 2019. Data on the daily time spent listening to AM/FM radio per adult in the United States from 2015 to 2020 reveals a gradual decrease in radio listening for the most part, though the biggest drop occurred between 2017 and 2018.

The following information comprises details of the technical parameters of all analogue VHF, MF, and DAB transmitters (including services on multiplexes) currently on-air. In-use parameters are included mainly for listeners and aerial installers who may wish to know more about the nature of the signals they are receiving and possibly optimise their aerial accordingly. The maximum allowed parameters (within the terms of each licence) for analogue services are also included for the benefit of those, principally in the industry, who wish to make calculations, particularly about interference, for coverage-planning purposes. The format is intended more for ease of download and transfer into specialist databases, than for presentation. Additionally, CSV data is provided for viewers who don’t use MS Excel or a compatible application. The TxParams data will be refreshed regularly as new transmitters are added, modified, or suppressed. This update contains some additional records for recently commissioned transmitting stations.

This dataset contains the model, dataset and training data used for training the CNN used in Spectrum Awareness winners of the Dyspan 2017 Challenge. If you use the this data for your own publications please cite: F. Paisana et al., "Context-aware cognitive radio using deep learning," 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Piscataway, NJ, 2017, pp. 1-2. doi: 10.1109/DySPAN.2017.7920784